Conference Agenda

Nonverbal communication is an important and helpful way to deliver messages. Motion is one element of nonverbal communication that can enhance the clarity of messages between the sender and receiver. However, the perception of motion varies based on the diverse backgrounds of participants, with the difference in cultural background being one thing that affects interpretation. Nevertheless, there is a lack of research on how people from different backgrounds perceive motion. This study aims to verify the factors influencing the perception of vitality and understand how individuals in various locations perceive motion. The experiment employed motion graphics, utilizing angle, acceleration, and fluctuation as tools to investigate their influence on the evaluation. This research implements an experiment with participants from Japan and Thailand, utilizing Head-Mounted Displays (HMDs). The study utilizes a 20-question subset from the Profile of Mood States 2nd Edition (POMS 2). Within this subset, 10 questions are categorized as positive, and 10 as negative sub-scales that correlate with the feeling of vitality. The results reveal differences in the evaluation of motion perception between Thai and Japanese participants. Specifically, Thai participants significantly rated attributes such as lively, vigorous, cheerful, active, alert, energetic, helpful, and efficient higher compared to Japanese participants (p < .01). Understanding cultural influences on perception leads to enhancing nonverbal communication and guiding diverse product development for varied target audiences. In terms of education, the use of learning materials designed to evoke positive emotions enhances comprehension. Researching methods to elicit positive emotions is essential for the future.

In design research there is a deep interest in how designers solve complex problems using design methods and heuristic shortcuts and in particular how this might relate to Machine Language (ML) to simulate the design process. With the introduction of Large Language Model (LLMs) such as Chat GPT we can appreciate how software with the remarkable capability of Generative Ai (Gen Ai) and generative design can be used to assist designers in the three-dimensional design of their products. In this paper, we will focus on how AI will impact designing in computing, identify what is relevant and suggest a new development opportunity. Our interest is in examining the potential for better and novel software solutions, making them easier to use during the design synthesis process and capable of adjustment throughout the 3D CAD development stage. The specific problem we aim to resolve is how to optimise a designer’s time spent from concept to production using Gen AI & 3D CAD software without affecting the quality of design thinking, methodology and practical process. Gen AI as an evolving platform has the potential to create a design to production productivity shift that industry and academic groups have long predicted. Designing will remain creative and inventive, individualistic or team based and using what we have termed an AI design assistant, AIDA.

Artificial Intelligence has the potential to substantially transform design education. Since ChatGPT was made available online only in November 2022, before course descriptions for the 2023/2024 academic year were finalised, there was no formal basis for regulating the use of this tool, or similar ones, in student assignments. This was particular an issue for a course that requires students to write a scientific review article, in the last year of master programs in Industrial Design Engineering, Industrial Design, and Interaction Design. In the autumn 2023 edition of this course, which has continuously run since 2001, 80+ students were tasked to write a 10-page review article on a topic of their personal interest, in conjunction with a design course where students are tasked to do a design project founded on state-of-the-art theoretical understanding in a relevant field.

Since the autumn of 2022, the university has not managed to provide guidance for teachers or students on AI use, other than some very general university-wide guidelines which mostly address plagiarism, and a notion that ‘potential challenges and opportunities that would result from using chatbots vary from discipline to discipline and course to course’. Taking up the challenge of establishing what would be good practice in the context of this particular course, for the first time a workshop was organised at the beginning of the course, to address specifically the issue of using AI in the process of writing a review article, and to provide clear guidelines for students on how to approach this issue.

The first part of this paper explains the organisation and content of the workshop, which challenged students to navigate the landscape of appropriate, undesirable, inappropriate and unacceptable use of AI tools like ChatGPT towards the preparation of their review article. The workshop resulted in establishing a contract between students and responsible teachers, which was intended to provide a clear set of rules and frame of reference for students to navigate in. The workshop also provided a stage to share fears, both from the side of the students and the teachers, for example about how to take up possible accusations or being unjustly accused of unacceptable AI use .

All students were asked to include, in the Methodology section of their review article, to explain how and why they did (or did not) use AI-based tools in doing the research for their article and during the writing process. A preliminary analysis of drafts of the paper indicates that some students used chatbots extensively, whereas others explicitly state to have refrained from their use completely. The second part of this paper analyses and the discusses choices that students made, also informed by an post-course survey in which students were asked to reflect over the use of AI in the course, and draws learning from the results of this towards next editions of this and other courses.

The aim of this paper is to consider the impact of AI in engineering design teaching in traditional MEng undergraduate courses. AI is presently making headlines, so it would be interesting to use and see what it produces for existing design coursework.

The coursework is in the first year and is intended to give students experience of the design process in which they also apply engineering drawing practice from semester 1. The students enrol onto our programmes with good analytical skills from their high grades in maths, and similar, so design and the teaching of it needs to pitched at a level which they can engage in and not be overwhelmed.

Equally, because coursework is “open” assessment they will and should be able to research any resource for knowledge and inspiration especially in the concept phase. So, does it matter if students use AI? After all, they ought to be technologically aware.

Here, the author will explore AI (software) by focusing on two existing design assessments and review the findings. One assessment is worked on in groups of six or seven and each student must contribute and concept drawing. Students have often commented that after generating three concepts, it is difficult to think of different ones. Maybe AI is helpful here as ideas generator and one can take a relaxed view of its usage. Conversely, it might provide images with poor functionality.

Either way, it will be useful as an educationalist to know more about AI and this will be of benefit to the author. As mentioned, students are new to design and need support to increase their appreciation of mechanisms and design elements, and to that end, the learning materials are chosen carefully to help them step through the process. If AI can short circuit that process, then the danger is that they “cut and paste” with little design learning. There is a thin here, as students can use the web, so how much further does AI really help in completing the assignment’s deliverables…

Co-design brings designers, end-users, researchers, and other pertinent stakeholders together to forge meaningful design solutions. It dismantles traditional barriers between professional designers and end-users by fostering collaborative, participatory design development processes. This paper explores using an AI visualisation tool, Vizcom, in a co-design workshop. The tool helps participants without visualisation skills to convert their rough sketches into refined visual representations. Thirty-six undergraduate students from Brigham Young University across ten disciplines participated in the study. Participants were introduced to the principles of co-design and the functionalities of the Vizcom, including how to create accounts, craft effective textual prompts for AI, and adjust the drawing influence parameter to optimise the visualisation of their ideas. Participants worked in pairs, designated as "users" and "professionals.” Prompted to reflect on their campus lunch food heating experiences, users shared insights with professionals who conducted interviews to pinpoint specific problems. Following this, professionals and users brainstormed solutions together. The users then sketched the proposed solutions, guided by the insights and ideas discussed during their collaborative session. After completing their sketches, they used their mobile phones to upload their sketches and detailed prompts into Vizcom, generating visual representations of their concept.

The study collected feedback from both professional and user roles through separate surveys, assessing the effectiveness of the AI in capturing and enhancing their conceptual solutions. The findings suggest new avenues for co-creation in product design, emphasising the potential of AI tools to bridge the gap between rudimentary sketches and sophisticated visual outputs.